Estimating Connecticut Stream Temperatures Using Predictive Models

The Connecticut Department of Energy and Environmental Protection (CT DEEP) seeks to better classify their streams into thermal regimes (cold, cold transitional, warm transitional, and warm water). A prediction model was created based upon physical characteristics such that CT DEEP could classify streams into thermal regimes based upon the parameters described in Lyons et al. 2009 and compare them to their own classification system. Accurately classifying these thermal regimes determines the environmental protection provided to a stream as well as the potential for establishing fisheries

Database Analysis to Support Nutrient Criteria Development (Phase II)

The intent of this publication of the Arkansas Water Resources Center is to provide a location whereby a final report on water research to a funding agency can be archived. The Texas Commission on Environmental Quality (TCEQ) contracted with University of Arkansas researchers for a multiple year project titled “Database Analysis to Support Nutrient Criteria Development”. This publication covers the second of three phases of that project and has maintained the original format of the report as submitted to TCEQ. This report can be cited either as an AWRC publication (see below) or directly as the final report to TCEQ

Experimental study on head loss due to cluster of randomly distributed non-uniform roughness elements in supercritical flow

Accurate estimation of head loss introduced via randomly placed roughness elements found in natural or constructed streams (e.g., fish passages) is essential in order to estimate flow variables in mountain streams, understand formation of niches for aquatic life, and model flow structure. Owing to the complexity of the involved processes and the often missing detailed data regarding the roughness elements, the head loss in such streams is mostly approximated using empirical models. In our study, we utilize flume experiments to analyze the effects of the spatial distribution of roughness elements on water surface levels and head loss and, moreover, use the produced data to test three empirical models estimating head loss. The experiments were performed in a 15 m long, 0.9 m wide flume with a slope of 5% under large Froude numbers (2.5–2.8). Flow velocities and water levels were measured with different flow rates at 58 points within a 3.96 m test section of the flume. We could show that different randomly arranged patterns of roughness elements significantly affected head loss (differences up to 33.6%), whereas water jumps occurred when flow depths were in the same size range as the roughness elements. The roughness element position and its size influenced water surface profiles. None of the three tested empirical models were able to well reproduce the differences in head loss due to the different patterns of roughness elements, with overestimated head loss from 12 to 94.7%, R2 from 41 to 73%, NSE from −21.1 to 0.09, and RRMSE from 18.4 to 93%. This generally indicates that these empirical models are conditionally suitable to consider head loss effects of random patterns of roughness elements